China

Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous hallucination detection methods, their evaluations often rely on ROUGE, a metric based on lexical overlap that misaligns with human judgments. Through comprehensive human studies, we demonstrate that while ROUGE exhibits high recall, its extremely low precision leads to misleading performance estimates. In fact, several established detection methods show performance drops of up to 45.9% when assessed using human-aligned metrics like LLM-as-Judge. Moreover, our analysis reveals that simple heuristics based on response length can rival complex detection techniques, exposing a fundamental flaw in current evaluation practices. We argue that adopting semantically aware and robust evaluation frameworks is essential to accurately gauge the true performance of hallucination detection methods, ultimately ensuring the trustworthiness of LLM outputs.

EMNLP 2025

The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs

llm reliability

hallucination detection

evaluation metrics

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

poster

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

With new large language models (LLMs) emerging frequently, it is important to consider the potential value of model-agnostic approaches that can provide interpretability across a variety of architectures. While recent advances in LLM interpretability show promise, many rely on complex, model-specific methods with high computational costs. To address these limitations, we propose NormXLogit, a novel technique for assessing the significance of individual input tokens. This method operates based on the input and output representations associated with each token. First, we demonstrate that during the pre-training of LLMs, the norms of word embeddings effectively capture token importance. Second, we reveal a significant relationship between a token's importance and the extent to which its representation can resemble the model's final prediction. Extensive analyses reveal that our approach outperforms existing gradient-based methods in terms of faithfulness and offers competitive performance in layer-wise explanations compared to leading architecture-specific techniques.

NormXLogit: The Head-on-Top Never Lies

As Large Language Models (LLMs) continue to scale, understanding how effectively their internal capacity is utilized becomes increasingly important, especially for inference-time efficiency. While existing scaling laws relate model size to loss and compute, they offer little insight into the representational dynamics of individual components. In this work, we focus on the Feed-Forward Network (FFN), a dominant sub-block in decoder-only transformers, and recast FFN width selection as a \emph{spectral utilization} problem. We introduce a lightweight, differentiable diagnostic suite comprising: \textbf{Hard Rank} (Participation Ratio), Soft Rank (spectral entropy), Spectral Concentration, and the composite Spectral Utilization Index (SUI), designed to quantify how many latent directions are meaningfully activated. Our spectral audit across GPT-2, LLaMA, and nGPT models reveals that spectral utilization grows with model size but not monotonically with width, often peaking at intermediate dimensions (e.g., D=2048). We identify clear instances of \emph{spectral collapse}, where wider FFNs concentrate variance into a narrow subspace, leaving much of the latent space unused.

Spectral Scaling Laws in Language Models: emphHow Effectively Do Feed-Forward Networks Use Their Latent Space?

Although reasoning has become a central focus for large language models, there remains a significant gap in corpora and human evaluation methods for lower-resource languages. To address this, we introduce the Trojsten Benchmark -- a dataset of 1,108 high-school competition problems in mathematics, physics, and programming sourced from Slovak archives -- that challenges conventional grading due to its complex formatting and language. We further introduce a LLM-based rubric-based grading approach to generate detailed evaluation rubrics and grade open-ended solutions, with our experiments revealing an average absolute grading difference of just 1.05 points compared to human evaluators. Extensive experiments benchmark various prompting techniques and models -- including GPT-3.5-Turbo, GPT-4, GPT-4o, and open-weight models like Llama 3 -- highlighting both promising performance and persistent challenges in multistep reasoning assessment.

Trojsten Benchmark: Evaluating LLM Problem-Solving in Slovak STEM Competition Problems

Large language models (LLMs) excel at many NLP tasks, yet their multi-step logical reasoning remains unreliable. Existing solutions such as Chain-of-Thought prompting generate intermediate steps but provide no internal check of their logical coherence. In this paper, we use the ``QK-score'', a lightweight metric based on query–key alignments within transformer attention heads, to evaluate the logical reasoning capabilities of LLMs. Our method automatically identifies attention heads that play a key role in distinguishing valid from invalid logical inferences, enabling efficient inference-time evaluation via a single forward pass. It reveals latent reasoning structure in LLMs and provides a scalable mechanistic alternative to ablation-based analysis. Across three benchmarks: ProntoQA-OOD, PARARULE-Plus, and MultiLogicEval, with models ranging from 1.5B to 70B parameters, the selected heads predict logical validity up to 14% better than the model probabilities, and remain robust under distractors and increasing reasoning depth of dleq 6.

Quantifying Logical Consistency in Transformers via Query-Key Alignment

Large language models (LLMs) are increasingly used in interactive applications, and human evaluation remains the gold standard for assessing their performance in multi-turn conversations. Since human studies are costly, time-consuming, and hard to reproduce, recent work explores using LLMs to simulate users for automatic assistant evaluation. However, there is no benchmark or systematic study to evaluate whether these simulated users are reliable stand-ins for real users. To address this, we introduce USimBench, a benchmark of 909 annotated human–LLM conversations on two interactive tasks---math tutoring and document creation. USimBench evaluates simulators based on how closely their messages match human behavior and how well their assistant ratings align with human judgments. Experiments on various simulator methods show that simulators conditioned on user profiles, capturing traits like background and message styles, align closely with human judgments. They reach Spearman's ρ of 0.7 on both tasks, providing a practical, scalable alternative to human evaluation.

SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants?

Methods for lexical semantic change detection quantify changes in the meaning of words over time. Prior research produced many methods that excel when evaluated on established benchmarks that consist of annotated sets of pre-selected \emph{target} words; the targets are chosen ahead of time due to the prohibitively large cost of manually annotating all words. However, performance measured on small curated sets of words cannot reveal how well these methods would perform when applied to \emph{discovering} semantic changes among the \emph{full} corpus vocabulary, which is the actual end goal for many applications. In this paper, we implement a top-k set-up to evaluate semantic change discovery (SCDisc) despite lacking complete annotations (although a contribution of our efforts is a 50% extension of the annotations in the commonly used LiverpoolFC benchmark). We use our evaluation setup to investigate a battery of semantic change detection methods that utilize three different kinds of contextualized representations, as well as recent techniques specifically designed to reduce false-discovery rates, such as statistical significance tests and metric scaling. We find that despite great performance on pre-existing benchmarks, \emph{all} methods of semantic change detection struggle at ranking known changes higher compared to other words in the vocabulary when presented with a \emph{natural} distribution of instances. Finally, we verify that the majority of words with high detected change scores do not in fact experience meaning changes. In fact, for most of the methods less than a third of highest ranked changes were annotated to have changed in meaning. Given the large performance discrepancies between semantic-change scoring and discovery ``in the wild'', we recommend that researchers direct more attention to semantic change discovery and include it in their suite of evaluations when proposing new methods.

Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild

In this paper, we introduce Spotlight, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a document. Unlike traditional summaries, which prioritize comprehensive coverage, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. We formally differentiate spotlights from related constructs and support our analysis with a detailed benchmarking study using new datasets curated for this work. To generate high-quality spotlights, we propose a two-stage approach: fine-tuning a large language model on our benchmark data, followed by alignment via Direct Preference Optimization (DPO). Our comprehensive evaluation demonstrates that the resulting model not only identifies key elements with precision but also enhances readability and boosts the engagement value of the original document.

Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents

Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining (AM). This paper investigates the integration of state-of-the-art LLMs into ArgSum, including for its evaluation. In particular, we propose a novel prompt-based evaluation scheme, and validate it through a novel human benchmark dataset. Our work makes three main contributions: (i) the integration of LLMs into existing ArgSum frameworks, (ii) the development of a new LLM-based ArgSum system, benchmarked against prior methods, and (iii) the introduction of an advanced LLM-based evaluation scheme. We demonstrate that the use of LLMs substantially improves both the generation and evaluation of argument summaries, achieving state-of-the-art results and advancing the field of ArgSum. We also show that among the four LLMs integrated in (i) and (ii), Qwen-3-32B, despite having the fewest parameters, performs best, even surpassing GPT-4o, while LLaMA-3.3-70B consistently underperforms.

Argument Summarization and its Evaluation in the Era of Large Language Models

This work investigates capturing and modeling disagreement in Semantic Textual Similarity (STS), where sentence pairs are assigned ordinal similarity labels (0–5). Conventional STS systems average multiple annotator scores and focus on a single numeric estimate, overlooking label dispersion. By leveraging the disaggregated SemEval-2015 dataset (Soft-STS-15), the authors propose a disagreement-aware calibration strategy that treats STS as an ordinal distribution prediction problem. Using a cross-encoder trained with a distance-aware objective, they produce softer, probabilistic output and apply post-hoc temperature scaling to refine calibration. Results show improved performance in distance-based metrics and robust handling of ambiguous pairs, demonstrating that modeling disagreement benefits both calibration and ranking accuracy. The paper thus highlights the value of retaining and modeling full annotation distributions, rather than collapsing them to a single mean label.

Beyond Averages: Learning with Annotator Disagreement in STS

Topic models often fail to capture low-prevalence, domain-critical themes—so-called minority topics—such as mental health themes in online comments. While some existing methods can incorporate domain knowledge such as expected topical content, methods allowing guidance may require overly detailed expected topics, hindering the discovery of topic divisions and variation. We propose a topic modeling solution via a specially constrained NMF. We incorporate a seed word list characterizing minority content of interest, but we do not require experts to pre-specify their division across minority topics. Through prevalence constraints on minority topics and seed word content across topics, we learn distinct data-driven minority topics as well as majority topics. The constrained NMF is fitted via Karush-Kuhn-Tucker (KKT) conditions with multiplicative updates. We outperform several baselines on synthetic data in terms of topic purity, normalized mutual information, and also evaluate topic quality using Jensen-Shannon divergence (JSD). We conduct a case study on YouTube vlog comments, analyzing viewer discussion of mental health content; our model successfully identifies and reveals this domain relevant minority content.

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NormXLogit: The Head-on-Top Never Lies

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